AAAI 2018 Robert S. Engelmore Memorial Award Lecture
Stephen F. Smith
Carnegie Mellon University
Talk: Smart Infrastructure for Future Urban Mobility
Abstract: Real-time traffic signal control presents a challenging multi-agent planning problem, particularly in urban road networks where (unlike simpler arterial settings) there are competing dominant traffic flows that shift through the day. Further complicating matters, urban environments require attention to multi-modal traffic flows (vehicles, pedestrians, bicyclists, buses) that move at different speeds and may be given different priorities. For the past several years, my research group has been evolving an adaptive traffic signal control system to address these challenges, referred to as Surtrac (Scalable Urban TRAffic Control). Combining principles from automated planning and scheduling, multi-agent systems, and traffic theory, Surtrac treats traffic signal control as a decentralized online planning process. In operation, each intersection repeatedly generates and executes (in rolling horizon fashion) signal-timing plans that optimize the movement of currently sensed approaching traffic through the intersection. Each time a new plan is produced (nominally every couple of seconds) the intersection communicates to its downstream neighbors what traffic it expects to send their way (according to the plan), allowing intersections to construct longer horizon plans and achieve coordinated behavior. Initial deployment of Surtrac in the East End of Pittsburgh PA has produced significant performance improvements and the technology is now being installed in other US cities. More recent work focuses on a broader future vision of smart transportation infrastructure where, as vehicles become more connected and more autonomous, the intersection increasingly becomes the gateway to real-time traffic information and navigation intelligence. Current technology development efforts center on use of direct vehicle- (and pedestrian-) to-infrastructure communication to further enhance mobility, online analysis of traffic flow information for real-time incident detection, and integrated optimization of signal control and route choice decisions. In this talk, I’ll provide an overview of this overall research effort.
Stephen Smith is a Research Professor in the Robotics Institute at Carnegie Mellon University, where he heads the Intelligent Coordination and Logistics Laboratory. He is also Co-founder and CEO of Rapid Flow Technologies, an intelligent transportations systems (ITS) technology company that is commercializing the Surtrac traffic signal control system. Smith’s research focuses broadly on the theory and practice of next-generation technologies for planning, scheduling, and coordination. He pioneered the development and use of constraint-based search and optimization models for solving planning and scheduling problems, and he has successfully fielded AI-based planning and scheduling systems in several complex application domains. Smith has published over 270 papers on these and related subjects. He recently served as a member of the AAAI Executive Council (2014-2017), is Associate Editor of the Journal of Scheduling, and serves on the editorial boards of Constraints and ACM Transactions on Intelligent Systems and Technology. He was elected AAAI Fellow in 2007.
IAAI-18 Invited Speakers
Talk: Leveraging AI and geospatial data to understand the Earth at scale
Abstract: Orbital Insight is a Geospatial analytics company leveraging the rapidly growing availability of satellite, UAV, and other geospatial data sources, to understand and characterize socio-economic trends at global, regional, and hyper-local scales. In this talk I’ll first discuss the satellite imagery domain, how it’s evolving, and the various advantages and challenges of working with such imagery. I will also cover several examples of computer vision modules we have built using deep learning, and some lessons learned. Finally, I’ll talk about practical considerations of rapidly prototyping and productionizing computer vision / deep learning models.
Boris Babenko received a PhD in computer science at UC San Diego, where he studied weakly supervised learning and its applications to object detection, recognition and tracking. After graduate school Boris dabbled in entrepreneurship, co-founding Anchovi Labs to offer computer vision and crowdsourcing as a service. The startup was acquired by Dropbox, where Boris spent the subsequent two and a half years working on the photos team and building the company’s first computer vision enabled product feature. Boris is now a Computer Vision Engineer at Orbital Insight where he works on deep learning and other computer vision technologies to help understand the Earth at scale.
University of Cambridge / Uber
AAAI/IAAI Joint Invited Talk
Title: Probabilistic Machine Learning and AI
Abstract: Probability theory provides a mathematical framework for understanding learning and for building rational intelligent systems. I will review the foundations of the field of probabilistic AI. I will then highlight some current areas of research at the frontiers, touching on topics such as Bayesian deep learning, probabilistic programming, Bayesian optimisation, and AI for data science.
Zoubin Ghahramani FRS is Professor of Information Engineering at the University of Cambridge and Chief Scientist at Uber. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence, and a Fellow of St John’s College. He was a founding Cambridge Director of the Alan Turing Institute, the UK’s national institute for data science. He has worked and studied at the University of Pennsylvania, MIT, the University of Toronto, the Gatsby Unit at University College London, and Carnegie Mellon University. His research focuses on probabilistic approaches to machine learning and artificial intelligence, and he has published over 250 research papers on these topics. He was co-founder of Geometric Intelligence (now Uber AI Labs) and advises a number of AI and machine learning companies. In 2015, he was elected a Fellow of the Royal Society for his contributions to machine learning.